The use of a machine-learning algorithm that predicts hypotension during surgery in combination with personalized treatment guidance: study protocol for a randomized clinical trial.

BACKGROUND: Intraoperative hypotension is associated with increased morbidity and mortality. Current treatment is mostly reactive. The Hypotension Prediction Index (HPI) algorithm is able to predict hypotension minutes before the blood pressure actually decreases. Internal and external validation of this algorithm has shown good sensitivity and specificity. We hypothesize that the use of this algorithm in combination with a personalized treatment protocol will reduce the time weighted average (TWA) in hypotension during surgery spent in hypotension intraoperatively. METHODS/DESIGN: We aim to include 100 adult patients undergoing non-cardiac surgery with an anticipated duration of more than 2 h, necessitating the use of an arterial line, and an intraoperatively targeted mean arterial pressure (MAP) of > 65 mmHg. This study is divided into two parts; in phase A baseline TWA data from 40 patients will be collected prospectively. A device (HemoSphere) with HPI software will be connected but fully covered. Phase B is designed as a single-center, randomized controlled trial were 60 patients will be randomized with computer-generated blocks of four, six or eight, with an allocation ratio of 1:1. In the intervention arm the HemoSphere with HPI will be used to guide treatment; in the control arm the HemoSphere with HPI software will be connected but fully covered. The primary outcome is the TWA in hypotension during surgery. DISCUSSION: The aim of this trial is to explore whether the use of a machine-learning algorithm intraoperatively can result in less hypotension. To test this, the treating anesthesiologist will need to change treatment behavior from reactive to proactive. TRIAL REGISTRATION: This trial has been registered with the NIH, U.S. National Library of Medicine at ClinicalTrials.gov, ID: NCT03376347 . The trial was submitted on 4 November 2017 and accepted for registration on 18 December 2017.

Patient blood management in the cardiac surgical setting: An updated overview.

In cardiac surgical patients it is a complex challenge to find the ideal balance between anticoagulation and hemostasis. Preoperative anemia and perioperative higher transfusion rates are related to increased morbidity and mortality. Patient blood management (PBM) is an evidence based patient specific individualized protocol used in the perioperative setting in order to reduce perioperative bleeding and transfusion rates and to improve patient outcomes. The three pillars of PBM in cardiac surgery consist of optimization of preoperative erythropoiesis and hemostasis, minimizing blood loss, and improving patient specific physiological reserves. This narrative review focuses on the challenges with special emphasis on PBM in the preoperative phase and intraoperative transfusion management and hemostasis in cardiac surgery patients. It is a "must" that PBM is a collaborative effort between anesthesiologists, surgeons, perfusionists, intensivists and transfusion laboratory teams. This review represents an up to date overview over "PBM in cardiac surgery patients".